Conference Tools for 2020 TMS Annual Meeting & Exhibition

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About this Abstract
Meeting	2020 TMS Annual Meeting & Exhibition
Symposium	Characterization: Structural Descriptors, Data-Intensive Techniques, and Uncertainty Quantification
Presentation Title	Parametric Models for Crystallographic Texture: Estimation and Uncertainty Quantification
Author(s)	Stephen R. Niezgoda, James Matuk, Oksana Chkrebtii
On-Site Speaker (Planned)	Stephen R. Niezgoda
Abstract Scope	An Orientation Distribution Function (ODF) describes the orientation of crystals in a polycrystalline material. While non-parametric kernel density estimation provides a quick method for estimating an ODF, there is limited interpretability. In this talk, we instead propose that an ODF takes a parametric form as a mixture of symmetric Bingham distributions. We treat the number of components, the mixture weights, and the scale and location parameters that determine the symmetric Bingham distribution as random variables through the Bayesian paradigm. Posterior distribution inference of the parameters through Bayesian methodology allows for interpretability and structured uncertainty quantification of the parameters of interest. We will additionally discuss the associated reversible jump Markov chain Monte Carlo algorithm which allows one to sample from the target posterior distribution. We will conclude with analyses of various data sets with interpretations of parameters of interest and a comparison with kernel density estimation methods.
Proceedings Inclusion?	Planned: Supplemental Proceedings volume

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A New Crystallographic Defect Quantification Workflow via Advanced-microscopy-based Deep Learning

Advancement of Data Intensive Approaches in Materials Discovery and Design

Adversarial Networks for Microstructure Generation and Modeling Phase Transformation Kinetics

Application of Machine Learning to Microstructure Quantification and Understanding

Artificial Intelligence Approaches to Microstructural Science

Automated Anomaly Detection in Unlabeled Computed Tomography Images

Basis Functions for Quantifying Grain Boundary Texture in Polycrystalline Microstructures

Characterizing GB Atomic Structures at Multiple Scales

Characterizing the Energetics and Structural Configurations of Silicon Carbide Grain Boundaries Using High-throughput Atomistic Techniques

Deep Convolutional Networks for Image Reconstruction from 3D Coherent X-ray Diffraction Imaging Data

Determination of Representative Volume Elements for Small Cracks in Heterogeneous Domains via Convolutional Neural Networks

Feature Engineering of Material Structure for Extracting Process-structure-property Linkages

GB Property Localization: Inference and Uncertainty Quantification of Grain Boundary Structure-property Models

Higher Order Spectral Terms in Grain Boundary Networks

Indexing of Electron Back-Scatter Diffraction Patterns Using a Convolutional Neural Network

Integrated Structural Methods Addressing Aviation Challenges in Composites

Investigating the Atomistic Nature of Grain Boundary Failure

Investigating the Effect of Solute Segregation to Grain Boundaries in Nanocrystalline Alloys Toward Stability and Strengthening

Investigations of Microstructural Effects on Porosity Evolution

Large-scale Defect Contrast Simulations for Scanning and Transmission Electron Microscopy

Large Scale Microstructure Synthesis Using LEGOMAT: Application to Additive Manufacturing

Machine Learning and Electron Backscatter Diffraction

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Utilizing Convolutional Neural Networks for Prediction of Process and Material Parameters from Microstructural Images

X-Ray Computed Tomography of 3D Crack Lattices in Advanced Ceramics and their Effect on Mechanical Response

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